Landfill gas detection method, device and equipment and storage medium

Abstract

The invention provides a landfill gas detection method, device and equipment and a storage medium, and relates to the technical field of landfill gas detection.The method comprises the steps that landfill gas concentration data of a selected monitoring well in continuous x days are collected to serve as historical data, and gas concentrations in the next few days are predicted; gas concentration detection does not need to be carried out every day, so that the working time is shortened, and fuzzy detection on time is realized; the n representative monitoring wells are selected from the m monitoring wells of the to-be-detected area for data collection, the gas concentration in the area is further predicted, and fuzzy detection on the area is achieved. In this way, the gas concentration of the to-be-detected area is detected in a fuzzy mode in time and area, and the cost and workload of gas detection are greatly reduced. In addition, the fuzzy detection methods on time and area can be independently applied and can also be applied in a combined manner, so that the method can be applied to different landfill gas detection scenes.

Description

Technical Field

[0001] The present application relates to the technical field of landfill gas detection, and in particular to a landfill gas detection method, device, equipment and storage medium. Background Art

[0002] Landfill gas is a mixed gas with methane and carbon dioxide as the main components, which is produced by the decomposition of domestic waste by microorganisms in the landfill. The domestic waste landfill materials in the domestic waste landfill will produce a large amount of landfill gas in the long-term reaction. The main components of landfill gas include CH4, H2S, CO, CO2, and O2. Some of these gases are flammable and explosive dangerous gases, and some are toxic and harmful gases that pollute the environment. In the process of landfill restoration, it is necessary to extract, collect and analyze the landfill gas in various areas of the landfill covering hundreds of acres. The huge area leads to a large number of monitoring wells and a huge monitoring workload, resulting in huge time and monitoring costs, as well as low efficiency. Summary of the invention

[0003] In view of the above problems, the present application provides a landfill gas detection method, including the following contents:

[0004] In a first aspect, the present application provides a landfill gas detection method, the method comprising:

[0005] Acquire the landfill gas concentration data of the selected monitoring wells in the area to be detected for x consecutive days as historical data; the selected monitoring wells are all the monitoring wells in the area to be detected or n monitoring wells among the m monitoring wells in the area to be detected;

[0006] Determine the gas concentration change trend on day x+y based on the historical data;

[0007] Predicting the landfill gas concentration of the area to be detected on the x+y day based on the gas concentration data on the xth day, the gas concentration change trend on the x+yth day, and the value corresponding to y;

[0008] Or obtain landfill gas concentration data of n monitoring wells among m monitoring wells in the area to be detected; obtain the landfill gas concentration of the area to be detected on a certain day according to the weighted average of the landfill gas concentration data of the n monitoring wells, and n is less than m.

[0009] Optionally, determining the gas concentration change trend on day x+y based on the historical data includes:

[0010] The gas change trend within x days in the historical data is used as the estimated gas change trend on day x+y, where the change trend includes increasing, decreasing or unchanged;

[0011] Calculate the gas concentration change rate of each day from the second day within the x days, substitute all the gas concentration change rates into the preset membership function corresponding to the expected gas change trend for calculation, obtain the membership value, and determine the gas concentration change trend on the x+y day according to the membership value.

[0012] Optionally, determining the gas concentration change trend on day x+y based on the historical data includes:

[0013] Calculate the gas concentration change rate of each day starting from the second day within x days in the historical data;

[0014] Substitute all gas concentration change rates into preset membership functions representing an upward trend, a downward trend, and a constant trend for calculation to obtain membership values, and determine the gas concentration change trend on the x+y day based on the membership values.

[0015] Optionally, when the gas concentration change trend on the x+y day is an upward trend, the gas concentration change trend on the x+y day is a positive value;

[0016] When the gas concentration change trend on the x+y day is a downward trend, the gas concentration change trend on the x+y day is a negative value;

[0017] When the changing trend of the gas concentration on the x+y day is a constant trend, the changing trend of the gas concentration on the x+y day is zero.

[0018] Optionally, when the selected monitoring wells are n monitoring wells among the m monitoring wells in the area to be detected, the acquisition of landfill gas concentration data of the selected monitoring wells in the area to be detected for x consecutive days as historical data includes:

[0019] The landfill gas concentration data of the area to be detected within x consecutive days are obtained as historical data, wherein the landfill gas concentration data of each day within the x days are obtained based on the weighted average of the landfill gas concentration data of n monitoring wells.

[0020] In a second aspect, the present application provides a landfill gas detection device, the device comprising:

[0021] A first acquisition unit is used to acquire landfill gas concentration data of selected monitoring wells in the area to be detected for x consecutive days as historical data; the selected monitoring wells are all monitoring wells in the area to be detected or n monitoring wells among m monitoring wells in the area to be detected;

[0022] A first data processing unit, configured to determine a gas concentration change trend on day x+y based on the historical data;

[0023] The first data processing unit is further used to predict the landfill gas concentration of the to-be-detected area on the x+y day based on the gas concentration data on the xth day, the gas concentration change trend on the x+yth day, and the value corresponding to y;

[0024] or a second acquisition unit, the second acquisition unit being used to acquire landfill gas concentration data of n monitoring wells among m monitoring wells in the area to be detected;

[0025] The second data processing unit is used to obtain the landfill gas concentration of the to-be-detected area on a certain day according to the weighted average value of the landfill gas concentration data of the n monitoring wells, where n is less than m.

[0026] Optionally, the first data processing unit is specifically used for:

[0027] The gas change trend within x days in the historical data is used as the estimated gas change trend on day x+y, where the change trend includes increasing, decreasing or unchanged;

[0028] Calculate the gas concentration change rate of each day from the second day within the x days, substitute all the gas concentration change rates into the preset membership function corresponding to the expected gas change trend for calculation, obtain the membership value, and determine the gas concentration change trend on the x+y day according to the membership value.

[0029] Optionally, the first data processing unit is specifically used to: calculate the gas concentration change rate of each day starting from the second day within x days in the historical data;

[0030] Substitute all gas concentration change rates into preset membership functions representing an upward trend, a downward trend, and a constant trend for calculation to obtain membership values, and determine the gas concentration change trend on the x+y day based on the membership values.

[0031] Optionally, when the gas concentration change trend on the x+y day is an upward trend, the gas concentration change trend on the x+y day is a positive value;

[0032] When the gas concentration change trend on the x+y day is a downward trend, the gas concentration change trend on the x+y day is a negative value;

[0033] When the changing trend of the gas concentration on the x+y day is a constant trend, the changing trend of the gas concentration on the x+y day is zero.

[0034] Optionally, the device further comprises a third acquisition unit, configured to acquire landfill gas concentration data of the to-be-detected area within x consecutive days as historical data, wherein the landfill gas concentration data of any day within the x days is obtained based on a weighted average of landfill gas concentration data of n monitoring wells, and there are m monitoring wells in the to-be-detected area, where n is less than m;

[0035] The third data processing unit is used to determine the gas concentration change trend on the x+y day based on the historical data; and predict the landfill gas concentration on the x+y day in the area to be detected according to the gas concentration data on the xth day, the gas concentration change trend on the x+y day and the value corresponding to y.

[0036] In a third aspect, the present application provides a device comprising a memory and a processor, wherein the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device performs the landfill gas detection method introduced in any implementation of the first aspect.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium having codes stored therein. When the codes are executed, a device executing the codes implements the landfill gas detection method described in any implementation of the first aspect.

[0038] The present application provides a method for detecting landfill gas. The method includes obtaining landfill gas concentration data of selected monitoring wells in the area to be detected for x consecutive days as historical data, determining the gas concentration change trend on the x+y day based on the historical data, predicting the landfill gas concentration on the x+y day in the area to be detected according to the gas concentration data on the x day, the gas concentration change trend on the x+y day and the value corresponding to y, or obtaining landfill gas concentration data of n monitoring wells among m monitoring wells in the area to be detected; obtaining the landfill gas concentration of the area to be detected on a certain day according to the weighted average of the landfill gas concentration data of the n monitoring wells, where n is less than m. By collecting the landfill gas concentration data of the selected monitoring wells for x consecutive days as historical data, the gas concentration in the next few days is predicted, and there is no need to perform gas concentration detection every day, which shortens the working time; by selecting n representative monitoring wells in the area to be detected for data collection, the gas concentration in the area is further predicted. In this way, the gas concentration in the area to be detected is detected by performing fuzzy detection in time, fuzzy detection in area, and combining fuzzy detection in time and area to detect the gas concentration in the area to be detected, thereby greatly reducing the cost and workload of gas detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 An overall schematic diagram of a landfill gas collection and analysis system provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the structure inside a monitoring well provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the internal process flow of a gas analysis cabinet provided in an embodiment of the present application;

[0043] Figure 4 A flow chart of a landfill gas detection method provided in an embodiment of the present application;

[0044] Figure 5 A flow chart of another landfill gas detection method provided in an embodiment of the present application;

[0045] Figure 6 A flow chart of another landfill gas detection method provided in an embodiment of the present application;

[0046] Figure 7 A schematic structural diagram of a landfill gas detection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] In a landfill soil remediation project, there are more than 70 monitoring wells, each of which has nearly 150 detection pipelines. It takes an average of 5 minutes to detect gas in each pipeline (including gas collection, gas dehumidification and drainage, entering 5 gas analyzers in sequence, and evacuating the pipeline air, etc.), which takes a total of nearly 12 hours. Figure 1 As shown, Figure 1An overall schematic diagram of a landfill gas collection and analysis system provided in an embodiment of the present application, wherein landfill gas is obtained from a monitoring well, passed into a gas analysis cabinet through a gas duct for analysis, the gas analysis cabinet is connected to a digital acquisition controller in a central control room through an optical fiber, and the gas analysis is realized through a control system. Figure 2 A schematic diagram of the structure of the interior of a monitoring well provided in an embodiment of the present application, each monitoring well has three gas samplers: high, medium and low. Figure 3 A schematic diagram of the internal process flow of a gas analysis cabinet provided in an embodiment of the present application, in which gas is extracted from the collection tubes or samplers at high, medium and low positions of the monitoring well in turn through a sampling pump and a switching valve. Only one pipeline gas enters five gas analyzers for gas composition analysis each time, and refrigeration, water removal, drainage, filtration, metering and other steps are performed before entering the gas analyzer. After the gas analysis of each branch pipe is completed, the switching valve will select the atmospheric circuit and inject air to empty and clean the entire process pipeline. The entire detection process is long, and when the number of detections is too frequent, the gas detector calibration times are also frequent, resulting in increased equipment loss, equipment calibration costs, etc.

[0049] In order to improve the monitoring efficiency, the present application optimizes the sampling order and frequency of the monitoring wells by adopting the fuzzy control method. The fuzzy control is carried out from two perspectives: A. fuzzy detection in time; B. fuzzy detection in area.

[0050] The following is an explanation of some professional terms involved in this application:

[0051] ① Landfill gas (LFG): Landfill gas is a mixed gas with methane and carbon dioxide as the main components produced by the decomposition of domestic waste by microorganisms in the landfill. LFG is the abbreviation of landfill gas (Landfill gas)

[0052] ② Monitoring well: It is a cylindrical pipe well distributed in the middle of the landfill. The depth of the well varies from 4 to 20 meters. The internal well pipe material is HDPE. The well pipe itself is a flower pipe with a large number of ventilation holes.

[0053] ③ Gas analysis cabinet: The composition of the gas analysis cabinet: 1. Gas pretreatment equipment: sampling pump, rapid cooling water remover, drainage pump, filter element, filter membrane. 2. Gas path control equipment: gas flow meter, gas path selection switch valve. 3. Analysis unit system: 5 types of gas analysis sensors, PLC data processing unit, etc.

[0054] ④ Fuzzy control: A control method that uses the basic ideas and theories of fuzzy mathematics. In the traditional control field, the accuracy of the control system's dynamic model is the most important factor affecting the quality of control. The more detailed the system's dynamic information is, the more accurate the control can be.

[0055] ⑤ Membership degree: If for any element x in the domain (scope of research) U, there is a corresponding A(x)∈[0,1], then A is called a fuzzy set on U, and A(x) is called the membership degree of x to A. When x changes in U, A(x) is a function, called the membership function of A. The closer the membership degree A(x) is to 1, the higher the degree to which x belongs to A, and the closer A(x) is to 0, the lower the degree to which x belongs to A. The membership function A(x) with a value in the interval (0,1) is used to represent the degree to which x belongs to A. The membership degree belongs to the concept of fuzzy evaluation function: Fuzzy comprehensive evaluation is a very effective multi-factor decision-making method for making a comprehensive evaluation of things affected by multiple factors. Its characteristic is that the evaluation result is not absolutely affirmative or negative, but is expressed as a fuzzy set.

[0056] Figure 4 This is a flow chart of a landfill gas detection method provided in an embodiment of the present application, in which a specific introduction is given to the time fuzzy detection. Figure 4 As shown, the landfill gas detection method provided in the embodiment of the present application may include:

[0057] S401. Obtain landfill gas concentration data of selected monitoring wells in the area to be detected for x consecutive days as historical data.

[0058] For x consecutive days, landfill gas is collected from all selected monitoring wells every day, and the gas concentration is analyzed by the landfill gas collection and analysis system. For example, landfill gas detection is performed in the area to be tested for 10 consecutive days.

[0059] S402: Determine the gas concentration change trend on day x+y based on the historical data.

[0060] In this embodiment, the landfill gas detection in the detection area is realized by using time fuzzy analysis, and there is no need to detect it every day. Therefore, after obtaining the gas concentration within x, the gas concentration in the next y days can be predicted, where x≥5 and y≤3.

[0061] Generally speaking, the concentration trend of a gas includes rising, falling or unchanged. It should be noted that unchanged means that the gas concentration changes very little, that is, within a certain time range, the fluctuation range of the gas concentration remains within an acceptable and relatively stable range, without obvious rising or falling trends. This unchanged trend may indicate that the state of the gas emission source is stable, or that the gas diffusion and dilution process in the monitoring area has reached a dynamic balance.

[0062] First, establish a fuzzy rule, including:

[0063] Rule A (Downward Trend): If the gas composition shows a downward trend for x consecutive days, it is inferred that the gas composition will continue to decline within the next y days.

[0064] Rule B (upward trend): If the gas composition shows an upward trend for x consecutive days, it is inferred that the gas composition will continue to rise within the next y days.

[0065] Rule C (small change): If the gas composition changes slightly over x consecutive days, it is inferred that the gas composition will also change slightly over the next y days.

[0066] First, according to the overall change trend of gas concentration in x days, the predicted gas change trend on the x+y day is determined, and the judgment is made according to the above fuzzy rules. For example, if the gas composition shows a downward trend for x consecutive days in the previous x days, it is inferred that the gas composition will continue to decrease in the next y days, that is, the predicted gas change trend on the x+y day is downward. In order to ensure the accuracy of the gas change trend, it is necessary to further judge through the membership function.

[0067] By calculating the gas concentration change rate of each day from the second day within the x days, all gas concentration change rates are substituted into the preset membership function corresponding to the expected gas change trend for calculation to obtain a membership value, and the gas concentration change trend of the x+y day is determined according to the membership value. The membership function is:

[0068] Membership function 1 (rule A):

[0069] Define the rate of change of gas concentration as ΔC t =C t -C t-1 , where Ct represents the gas concentration on day t. If the gas concentration has shown a downward trend over the past x days, the membership function can be defined as:

[0070]

[0071] Where ΔCmax and ΔCmin represent the maximum and minimum values ​​of the drop rate, respectively;

[0072] When the concentration change rate ΔC is less than or equal to -ΔCmax, the membership degree is 1, indicating that it fully complies with the downward trend;

[0073] When ΔC is greater than or equal to -ΔCmin, the degree of membership is 0, indicating that it does not conform to the downward trend.

[0074] Membership function 2 (rule B):

[0075] Similarly, when the gas concentration shows an upward trend, the membership function is defined as:

[0076]

[0077] Similar to the downward trend, ΔCmax and ΔCmin represent the maximum and minimum values ​​of the rising rate, respectively;

[0078] When the membership degree is 1, it means that it fully complies with the upward trend, and when the membership degree is 0, it does not comply with the upward trend.

[0079] Membership function 3 (rule C):

[0080] For the case of a small change, the membership function is defined as:

[0081]

[0082] |ΔC| represents the magnitude of change;

[0083] When the change amplitude is within the minimum change range ΔCmin, the membership degree is 1, indicating that it conforms to the stable trend; when the change amplitude exceeds ΔCmax, the membership degree is 0, indicating that it does not conform to the stable trend.

[0084] After obtaining the expected gas change trend, it is also necessary to calculate the gas concentration change rate of each day starting from the second day within x days. For example, when x is equal to 5, the gas concentrations within 5 days are C1=100, C2=98, C3=96, C4=94, C5=92, ΔC2=98-100=-2, ΔC3=96-98=-2, ΔC4=94-96=-2, ΔC5=92-94=-2.

[0085] Assume that the membership function of the downward trend is: μ down (ΔC) = (-ΔC-0) / (4-0)

[0086] =-ΔC / 4, substitute ΔC=-2, then: μ decreases by (-2)=2 / 4=0.5, indicating that the downward trend of gas concentration is moderate, that is, the concentration change trend of the gas is determined to be a downward trend.

[0087] In the above embodiment, an estimated gas change trend is obtained according to the gas concentration change trend within x days, and then all gas concentration change rates are substituted into the preset membership function corresponding to the estimated gas change trend for calculation to obtain the membership value, and then the gas concentration change trend on the x+y day is determined. In addition, another method is provided in the embodiment of the present application, which first calculates the gas concentration change rate of each day from the second day within x days in the historical data; substitutes all gas concentration change rates into the preset membership function representing the upward trend, downward trend and unchanged trend for calculation to obtain the membership value, and determines the gas concentration change trend on the x+y day according to the membership value. That is, the estimated gas change trend is not obtained, and the gas concentration change rate is directly calculated in the same way as in the above method, and then substituted into each membership function, and the sum or maximum value of the membership of different trend types is compared. For example, when the maximum or maximum value of the membership sum representing the downward trend in the calculation result is calculated based on the membership function of the downward trend, it is determined that the gas concentration change trend on the x+y day is a downward trend.

[0088] S403: predicting the landfill gas concentration of the area to be detected on the x+y day according to the gas concentration data on the xth day, the gas concentration change trend on the x+yth day, and the value corresponding to y.

[0089] After obtaining the gas concentration change trend on day x+y, use the formula

[0090] C x+y =C x +yΔC 趋势

[0091] Calculate the landfill gas concentration on the x+y day in the area to be detected. The gas concentration change trend on the x+y day takes different values ​​according to different trends. When the gas concentration change trend on the x+y day is an upward trend, the gas concentration change trend on the x+y day is a positive value; when the gas concentration change trend on the x+y day is a downward trend, the gas concentration change trend on the x+y day is a negative value; when the gas concentration change trend on the x+y day is a constant trend, the gas concentration change trend on the x+y day is zero.

[0092] For example, according to the data given in the above embodiment, the concentration in the next 3 days is predicted: according to the downward trend, the concentration decreases by 2 units every day, so the gas concentration on the 5th + 3th = 8th day is predicted to be:

[0093] C8=C5+3·(-2)=92+(-6)=86, therefore, the predicted gas concentration on the 8th day is 86.

[0094] The above embodiment introduces time-based fuzzy detection, which obtains landfill gas concentration data of the area to be detected for several consecutive days and predicts the gas concentration in the next few days. In this way, there is no need to perform gas concentration detection every day, which not only saves detection time but also reduces the number of times the detection equipment is used, thereby saving detection costs.

[0095] In addition to temporal blur detection, the present application also provides a regional blur detection method, which is introduced below in conjunction with specific embodiments. Figure 5 A flow chart of another landfill gas detection method provided in an embodiment of the present application, the method comprising:

[0096] S501. Obtain landfill gas concentration data of n monitoring wells among m monitoring wells in the area to be detected.

[0097] The area to be detected includes m monitoring wells. n monitoring wells are selected from the m monitoring wells for gas concentration detection. Multiple detections can be performed to ensure the accuracy of the data.

[0098] The following is an introduction to the specific selection strategy. The selection matrix is ​​set to S, with a dimension of k×m, where k represents k selections, with n wells selected each time. The elements of the matrix S are defined as: Sij=1 means that the jth well is selected in the i-th selection; Sij=0 means that the jth well is not selected in the i-th selection. Assume that the probability of each well being selected is pj (j=1,2,...,m), and the wells selected each time are independent.

[0099] The probability of each well being selected is pj, where j represents the well number and j = 1, 2, ..., m. The selection of wells can be represented by binomial distribution, and the probability of selecting a well is P(Sij = 1) = pj, that is, the probability of the jth well being selected in the ith time. Use a matrix to express each selection: For each selection of n wells, it can be represented by a combination, that is, selecting n wells from m wells, record

[0100] The mathematical expression for each selection can be expressed as:

[0101]

[0102] in:

[0103] P(Si) represents the joint probability of the i-th choice;

[0104] pj represents the probability of the jth well being selected;

[0105] Sij indicates whether the jth well is selected in the i-th selection.

[0106] For example, suppose there are m=10 monitoring wells in the area, numbered 1 to 10, and n=5 wells are selected for monitoring each time.

[0107] The selection matrix S represents a 3×10 selection matrix (assuming three selections are made):

[0108]

[0109] The first row indicates the first selection of wells 1, 3, 4, 7, and 9 (the rest are 0s indicating that they were not selected);

[0110] The second row indicates the second selection of wells 2, 5, 6, 8, and 10;

[0111] Row 3 indicates the third selection of wells 1, 4, 5, 7, and 10.

[0112] Assume that the probability of selecting well 1 is p1=0.5, the probability of selecting well 2 is p2=0.3, and the probabilities of the other wells are p3=0.6, p4=0.7, p5=0.4, etc.

[0113] The probability of the first choice can be expressed as:

[0114]

[0115] Substituting the values:

[0116] P(S1)=0.5·0.7·0.6·0.7·0.6·0.9·0.8·0.9=0.0516

[0117] This means that the joint probability of the first selection is 0.0516, and the calculation method of the joint probability of the second and third selections refers to the calculation method of the joint probability of the first selection. By calculating the joint probability of each selection, we can comprehensively consider multiple monitoring results or select the most representative one as the result, and then deduce the overall concentration level in the area. This method is more accurate than a single monitoring and reduces the impact of accidental errors.

[0118] S502. Obtain the landfill gas concentration of the area to be detected on a certain day according to the weighted average value of the landfill gas concentration data of the n monitoring wells, wherein n is less than m.

[0119] If n wells are randomly selected for the i-th time, the i-th comprehensive concentration Ci can be expressed as the weighted average concentration of these wells. The specific mathematical expression is:

[0120]

[0121] in:

[0122] Ci represents the comprehensive concentration of n wells selected for the ith time (i.e., the comprehensive regional detection result of this detection).

[0123] Cj represents the actual concentration value of the j-th well (i.e., the monitoring result of the j-th well).

[0124] Sij indicates whether the jth well is selected in the i-th selection:

[0125] Sij = 1 means that the jth well is selected in the i-th selection;

[0126] Sij=0 means that the jth well was not selected in the i-th selection.

[0127] m is the total number of wells in the region (eg, m=10).

[0128] n is the number of wells selected each time (eg n=5).

[0129] The denominator represents the number of wells selected in the i-th selection, which is equal to n (5 in this case).

[0130] For example, 5 wells are randomly selected from 10 wells, and the i-th selection is as follows:

[0131] Si=(1,0,1,1,0,0,1,0,1,0), which means that wells 1, 3, 4, 7, and 9 are selected in the i-th selection, and the concentration values ​​of these wells are: C1=8, C3=10, C4=6, C7=7, C9=9. According to the formula, the comprehensive concentration Ci is calculated as follows:

[0132]

[0133] Substitute specific values:

[0134]

[0135] Therefore, the comprehensive concentration selected this time is 8. This process can effectively integrate the monitoring results of randomly selected wells to deduce the overall concentration level in the area.

[0136] The above two embodiments respectively specifically introduce the time blur detection and the area blur detection. To further improve the detection efficiency, the time blur detection and the area blur detection can be used in combination. Figure 6 A flowchart of another landfill gas detection method provided in an embodiment of the present application, the specific implementation method includes:

[0137] S601. Obtain landfill gas concentration data of the area to be detected for x consecutive days as historical data, wherein the landfill gas concentration data of any day within the x days is obtained based on the weighted average of the landfill gas concentration data of n monitoring wells, and there are m monitoring wells in the area to be detected, where n is less than m.

[0138] From the m monitoring wells in the area to be detected, n monitoring wells (n is less than m) are selected to collect landfill gas concentration data for x consecutive days. For each of the x days, a weighted average is calculated based on the data of the selected n monitoring wells. This weighted average represents the landfill gas concentration data of that day, and a historical data set containing x days of landfill gas concentration data is obtained.

[0139] S602: Determine the gas concentration change trend on day x+y based on the historical data.

[0140] Based on the above historical data, refer to the implementation method in step S402 to determine the gas concentration change trend on the x+y day.

[0141] S603: predicting the landfill gas concentration of the area to be detected on the x+y day according to the gas concentration data on the xth day, the gas concentration change trend on the x+yth day, and the value corresponding to y.

[0142] Using the gas concentration data on day x, the gas concentration change trend on day x+y, and the corresponding value of y, the landfill gas concentration on day x+y is predicted.

[0143] The specific implementation methods in the above steps S601-S603 refer to the implementation methods in the above two embodiments, and will not be repeated here. The fuzzy detection methods in time and area in the above embodiments of the present application can be used independently or in combination. When used in combination, it means: a single detection represents a random detection, and analysis and prediction are performed based on the results of multiple detections, ultimately achieving the purpose of small-scale detection and efficient detection. When used independently, if the time measurement cycle in the actual production process is long, it is advisable to use a fuzzy detection method in the time dimension. If the measurement site area in the actual production process is wide, it is advisable to use a fuzzy detection method in the regional dimension. If the production cycle and site area are both large in magnitude, a combined detection method is used for the best effect.

[0144] The above are some specific implementations of a landfill gas detection method provided in the embodiment of the present application. Based on this, the present application also provides a corresponding device. The device provided in the embodiment of the present application will be introduced from the perspective of functional modularization.

[0145] Figure 7 A schematic structural diagram of a landfill gas detection device provided in an embodiment of the present application.

[0146] Combination Figure 7 As shown, the landfill gas detection device 700 provided in the embodiment of the present application includes:

[0147] The first acquisition unit 710 is used to acquire the landfill gas concentration data of the selected monitoring wells in the area to be detected for x consecutive days as historical data;

[0148] A first data processing unit 720, configured to determine a gas concentration change trend on day x+y based on the historical data;

[0149] The first data processing unit is further used to predict the landfill gas concentration of the to-be-detected area on the x+y day based on the gas concentration data on the xth day, the gas concentration change trend on the x+yth day, and the value corresponding to y;

[0150] or a second acquisition unit 730, the second acquisition unit is used to acquire landfill gas concentration data of n monitoring wells among m monitoring wells in the area to be detected;

[0151] The second data processing unit 740 is used to obtain the landfill gas concentration of the to-be-detected area on a certain day according to the weighted average value of the landfill gas concentration data of the n monitoring wells, where n is less than m.

[0152] In an implementation manner of the embodiment of the present application, the first data processing unit is specifically configured to:

[0153] The gas change trend within x days in the historical data is used as the estimated gas change trend on day x+y, where the change trend includes increasing, decreasing or unchanged;

[0154] Calculate the gas concentration change rate of each day from the second day within the x days, substitute all the gas concentration change rates into the preset membership function corresponding to the expected gas change trend for calculation, obtain the membership value, and determine the gas concentration change trend on the x+y day according to the membership value.

[0155] In an implementation of the embodiment of the present application, the first data processing unit is specifically used to: calculate the gas concentration change rate of each day from the second day within x days in the historical data;

[0156] Substitute all gas concentration change rates into preset membership functions representing an upward trend, a downward trend, and a constant trend for calculation to obtain membership values, and determine the gas concentration change trend on the x+y day based on the membership values.

[0157] In an implementation of the embodiment of the present application, when the gas concentration change trend on the x+y day is an upward trend, the gas concentration change trend on the x+y day is a positive value;

[0158] When the gas concentration change trend on the x+y day is a downward trend, the gas concentration change trend on the x+y day is a negative value;

[0159] When the changing trend of the gas concentration on the x+y day is a constant trend, the changing trend of the gas concentration on the x+y day is zero.

[0160] In an implementation of the embodiment of the present application, the device further includes a third acquisition unit, configured to acquire landfill gas concentration data of the to-be-detected area within x consecutive days as historical data, wherein the landfill gas concentration data of any day within the x days is obtained based on a weighted average of the landfill gas concentration data of n monitoring wells, and there are m monitoring wells in the to-be-detected area, where n is less than m;

[0161] The third data processing unit is used to determine the gas concentration change trend on the x+y day based on the historical data; and predict the landfill gas concentration on the x+y day in the area to be detected according to the gas concentration data on the xth day, the gas concentration change trend on the x+y day and the value corresponding to y.

[0162] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.

[0163] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the method described in any embodiment of the present application.

[0164] The computer storage medium stores codes, and when the codes are executed, a device executing the codes implements the method described in any embodiment of the present application.

[0165] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0166] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0167] It should also be noted that the various embodiments in this specification are described in a progressive manner, and the same and similar parts between the various embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments. The device and apparatus embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0168] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A landfill gas detection method, characterized in that: The method comprises: Acquire the landfill gas concentration data of the selected monitoring wells in the area to be detected for x consecutive days as historical data; the selected monitoring wells are all the monitoring wells in the area to be detected or n monitoring wells among the m monitoring wells in the area to be detected; Determine the gas concentration change trend on day x+y based on the historical data; Predicting the landfill gas concentration of the area to be detected on the x+y day based on the gas concentration data on the xth day, the gas concentration change trend on the x+yth day, and the value corresponding to y; Or obtain landfill gas concentration data of n monitoring wells among m monitoring wells in the area to be detected; obtain the landfill gas concentration of the area to be detected on a certain day according to the weighted average of the landfill gas concentration data of the n monitoring wells, and n is less than m.

2. The method according to claim 1, characterized in that: Determining the gas concentration change trend on day x+y based on the historical data includes: The gas change trend within x days in the historical data is used as the estimated gas change trend on day x+y, where the change trend includes increasing, decreasing or unchanged; Calculate the gas concentration change rate of each day from the second day within the x days, substitute all the gas concentration change rates into the preset membership function corresponding to the expected gas change trend for calculation, obtain the membership value, and determine the gas concentration change trend on the x+y day according to the membership value.

3. The method according to claim 1, characterized in that: Determining the gas concentration change trend on day x+y based on the historical data includes: Calculate the gas concentration change rate of each day starting from the second day within x days in the historical data; Substitute all gas concentration change rates into preset membership functions representing an upward trend, a downward trend, and a constant trend for calculation to obtain membership values, and determine the gas concentration change trend on the x+y day based on the membership values.

4. The method according to any one of claims 2 or 3, characterized in that: When the gas concentration change trend on the x+y day is an upward trend, the gas concentration change trend on the x+y day is a positive value; When the gas concentration change trend on the x+y day is a downward trend, the gas concentration change trend on the x+y day is a negative value; When the changing trend of the gas concentration on the x+y day is a constant trend, the changing trend of the gas concentration on the x+y day is zero.

5. The method according to claim 1, characterized in that When the selected monitoring well is n monitoring wells among the m monitoring wells in the area to be detected, the acquisition of landfill gas concentration data of the selected monitoring wells in the area to be detected for x consecutive days as historical data includes: The landfill gas concentration data of the area to be detected within x consecutive days are obtained as historical data, wherein the landfill gas concentration data of each day within the x days are obtained based on the weighted average of the landfill gas concentration data of n monitoring wells.

6. A landfill gas detection device, characterized in that: The device comprises: A first acquisition unit is used to acquire landfill gas concentration data of selected monitoring wells in the area to be detected for x consecutive days as historical data; the selected monitoring wells are all monitoring wells in the area to be detected or n monitoring wells among m monitoring wells in the area to be detected; A first data processing unit, configured to determine a gas concentration change trend on day x+y based on the historical data; The first data processing unit is further used to predict the landfill gas concentration of the to-be-detected area on the x+y day based on the gas concentration data on the xth day, the gas concentration change trend on the x+yth day, and the value corresponding to y; or a second acquisition unit, the second acquisition unit being used to acquire landfill gas concentration data of n monitoring wells among m monitoring wells in the area to be detected; The second data processing unit is used to obtain the landfill gas concentration of the to-be-detected area on a certain day according to the weighted average value of the landfill gas concentration data of the n monitoring wells, where n is less than m.

7. The device according to claim 6, characterized in that The first data processing unit is specifically used for: The gas change trend within x days in the historical data is used as the estimated gas change trend on day x+y, where the change trend includes increasing, decreasing or unchanged; Calculate the gas concentration change rate of each day from the second day within the x days, substitute all the gas concentration change rates into the preset membership function corresponding to the expected gas change trend for calculation, obtain the membership value, and determine the gas concentration change trend on the x+y day according to the membership value.

8. The device according to claim 6, characterized in that The first data processing unit is specifically used to: calculate the gas concentration change rate of each day starting from the second day within x days in the historical data; Substitute all gas concentration change rates into preset membership functions representing an upward trend, a downward trend, and a constant trend for calculation to obtain membership values, and determine the gas concentration change trend on the x+y day based on the membership values.

9. A computing device, characterized in that The computing device includes: a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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